ReviewCurrent treatment options in oncology2022
Proteomic and Metabolomic Profiling in Soft Tissue Sarcomas.
Review in Current treatment options in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Editorial: Therapeutic mechanism of osteosarcoma.Frontiers in molecular biosciences · 2025Article
- A comprehensive review on computational metabolomics: Advancing multiscale analysis throughComputational and structural biotechnology journal · 2025Review
- NR2F1 overexpression alleviates trophoblast cell dysfunction by inhibiting GDF15/MAPK axis in preeclampsia.Human cell · 2024Article
- Proteomic profiling improves prognostic risk stratification of the Sarculator nomogram in soft tissue sarcomas of the extremities and trunk wall.Cancer medicine · 2024Article
- Proteomic features of soft tissue tumours in adolescents and young adults.Communications medicine · 2024Article
- Translational Aspects of Epithelioid Sarcoma: Current Consensus.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Review
- Proteomic characterization identifies clinically relevant subgroups of soft tissue sarcoma.Nature communications · 2024Article
- The proteomic landscape of soft tissue sarcomas.Nature communications · 2023Article
- An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma.Biophysics reviews · 2023Review
- Proteomic profiling of canine fibrosarcoma and adjacent peritumoral tissue.Neoplasia (New York, N.Y.) · 2023Article
- On the Relevance of Soft Tissue Sarcomas Metabolic Landscape Mapping.International journal of molecular sciences · 2022Review
- Article
- Advances in the Clinical Application of High-throughput Proteomics.Exploratory research and hypothesis in medicineArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
opinion statementAdvances in proteomic and metabolomic technologies have accelerated our understanding of multiple aspects of cancer biology across distinct tumour types. Here we review the current state-of-the-art in the use of proteomics and metabolomics in soft tissue sarcomas. We highlight the utility of these Omics-based methodologies to identify new drug targets, synthetic lethal interactions, candidate therapeutics and novel biomarkers to facilitate patient stratification. Due to the unbiased and global nature of these profiling methods to assess the levels of protein expression, post-translational modifications such as phosphorylation and glycosylation as well as key metabolites, many of these findings have broad applications not just in specific histotypes but across multiple STS subtypes. Specific examples of proteomic and metabolomic findings that have led to the development of early phase clinical trials of investigational agents will be discussed. While promising, the use of these technologies in the study of sarcoma is still limited, and there is a need for further research in this area. In particular, it would be important to integrate these approaches with other Omics strategies such as genomics and epigenomics as well as implement these tools alongside clinical trials in order to maximize the impact of these tools on our biological understanding and treatment of this group of rare diseases of unmet need.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.